Online financial transactions are becoming more and more developed, and some users unlawfully use online finance to conduct illegal money operations, such as money laundering. In order to prevent the occurrence of illegal online financial transactions, there are already some solutions for identifying risk addresses, which are generally solutions targeted at identifying relatively regular risk addresses.
For example, word segmentation and labeling may be performed on an input address using a word segmentation algorithm, and finally address word matching is performed one by one according to the labeled information of different address words, so as to identify whether the input address is a risk address through the matching result.
Based on the prior art, a more accurate risk address identification solution is needed.